various fixes 20250305 (#2384)

* various validation fixes

* fix check for non-truthy value

src/axolotl/integrations/spectrum/args.py

@@ -17,7 +17,7 @@ Module for handling Spectrum input arguments.
 """
 from typing import Optional
 
-from pydantic import BaseModel
+from pydantic import BaseModel, model_validator
 
 
 class SpectrumArgs(BaseModel):
@@ -27,3 +27,20 @@ class SpectrumArgs(BaseModel):
 
     spectrum_top_fraction: Optional[float] = 0.5
     spectrum_model_name: Optional[str] = None
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_fsdp_use_orig_params(cls, data):
+        if (
+            data.get("fsdp")
+            and data.get("fsdp_config")
+            and not data["fsdp_config"].get("use_orig_params")
+            and data.get("plugins")
+            and any("SpectrumPlugin" in plugin for plugin in data["plugins"])
+        ):
+            # would otherwise raise
+            # ValueError: Must flatten tensors with uniform `requires_grad` when `use_orig_params=False`
+            raise ValueError(
+                "FSDP + SpectrumPlugin cannot be used together when `use_orig_params=False` is set"
+            )
+        return data

src/axolotl/utils/config/models/input/v0_4_1/__init__.py

@@ -778,9 +778,9 @@ class AxolotlInputConfig(
 
     # torch_dtype: Optional[torch.dtype]
 
-    gradient_checkpointing: Optional[Union[Literal["unsloth"], bool]] = Field(
+    gradient_checkpointing: Optional[
-        default=False
+        Union[Literal["unsloth", "offload"], bool]
-    )
+    ] = Field(default=False)
     gradient_checkpointing_kwargs: Optional[Dict[str, Any]] = None
 
     unfrozen_parameters: Optional[List[str]] = None
@@ -1154,6 +1154,15 @@ class AxolotlInputConfig(
             raise ValueError("gradient_checkpointing is not supported for MPT models")
         return self
 
+    @model_validator(mode="after")
+    def check_offload_grad_checkpointing(self):
+        if self.gradient_checkpointing and self.gradient_checkpointing == "unsloth":
+            LOG.warning(
+                "`unsloth` is deprecated for gradient_checkpointing, use `offload`"
+            )
+            self.gradient_checkpointing = "offload"
+        return self
+
     @model_validator(mode="after")
     def check_better_transformers(self):
         if self.flash_optimum is True:

src/axolotl/utils/gradient_checkpointing/__init__.py

@@ -4,7 +4,7 @@ from axolotl.utils.gradient_checkpointing.unsloth import (
 )
 
 
-def hf_grad_checkpoint_unsloth_wrapper(
+def hf_grad_checkpoint_offload_wrapper(
     decoder_layer, *args, use_reentrant=None
 ):  # pylint: disable=unused-argument
     return Unsloth_Offloaded_Gradient_Checkpointer.apply(

src/axolotl/utils/models.py

@@ -64,7 +64,7 @@ from axolotl.utils.distributed import (
     is_local_main_process,
     zero_only,
 )
-from axolotl.utils.gradient_checkpointing import hf_grad_checkpoint_unsloth_wrapper
+from axolotl.utils.gradient_checkpointing import hf_grad_checkpoint_offload_wrapper
 from axolotl.utils.lora_embeddings import get_linear_embedding_layers
 from axolotl.utils.model_shard_quant import load_sharded_model, load_sharded_model_quant
 
@@ -493,8 +493,8 @@ class ModelLoader:
 
             patch_fa_peft_integration()
 
-        if self.cfg.gradient_checkpointing == "unsloth":
+        if self.cfg.gradient_checkpointing in ["unsloth", "offload"]:
-            transformers.modeling_utils.checkpoint = hf_grad_checkpoint_unsloth_wrapper
+            transformers.modeling_utils.checkpoint = hf_grad_checkpoint_offload_wrapper
 
         if self.cfg.flash_attention:
             self.patch_attention()